Regarding object motion tracking and monitoring, indoor location-based service is becoming more and more important nowadays. One popular approach is to use dead-reckoning method to estimate the location of a moving object in real time. Usually, the moving direction and the moving distance are estimated by inertia measurement unit (IMU). However, the performance of moving distance estimation in the dead-reckoning based approach is far from satisfactory, which is the main reason that such indoor navigation systems are still not popular now.
Estimating the speed of a moving object in an indoor environment, which may assist the location-based service, is also an open problem and no satisfactory results appear yet. The Doppler effect has been widely applied to different speed estimation systems using sound wave, microwave, or laser light. However, low speed such as human walking speed is very difficult to be estimated using Doppler shift, especially using electromagnetic (EM) waves. This is because the maximum Doppler shift is about Δf=v/cf0, where f0 is the carrier frequency of the transmitted signal, c is the speed of light, and v is the human walking speed. Under normal human walking speed v=5.0 km/h and f0=5.8 GHz, Δf is around 26.85 Hz and it is extremely difficult to estimate this tiny amount with high accuracy. In addition, these methods need line-of-sight (LOS) condition and perform poorly in a complex indoor environment with rich multipath reflections.
Most of the existing speed estimation methods that work well for outdoor environments fail to offer satisfactory performance for indoor environments, since the direct path signal is disturbed by the multipath signal in indoor environments and the time-of-arrival (or Doppler shift) of the direct path signal cannot be estimated accurately. Then, researchers focus on the estimation of the maximum Doppler frequency which may be used to estimate the moving speed. Various methods have been proposed, such as level crossing rate methods, covariance based methods, and wavelet based methods. However, these estimators provide results that are unsatisfactory because the statistics used in these estimators have a large variance and are location-dependent in practical scenarios. For example, the accuracy of one existing speed estimation method may only differentiate whether a mobile station moves with a fast speed (above 30 km/h) or with a slow speed (below 5 km/h).
Another kind of indoor speed estimation method based on the traditional pedestrian dead reckoning algorithm is to use accelerometers to detect steps and to estimate the step length. However, pedestrians often have different stride lengths that may vary up to 40% at the same speed, and 50% with various speeds of the same person. Thus, calibration is required to obtain the average stride lengths for different individuals, which is impractical in real applications and thus has not been widely adopted.
A transient motion is a special type of motion that happens suddenly and quickly and is important to monitor. For example, a fall-down action of a person is a transient motion that happens very fast, typically in 0.5 to 1 second, and can indicate an abnormal or unexpected motion of the person. Existing systems utilize camera or video to monitor a transient motion like fall-down. But these existing systems do not work in a dark environment due to low brightness or in a venue like restroom due to privacy. Other existing transient motion monitoring techniques based on speed estimation will suffer the same issues as those mentioned above regarding existing speed estimation methods.
Therefore, there is a need for apparatus and methods for tracking and monitoring motion (especially a transient motion) to solve the above-mentioned problems and to avoid the above-mentioned drawbacks.